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• Are Men and Women Really Different? The Science and Myths of Sex Differences

  A calm look at common beliefs about differences between men and women, weighed against the research. We examine average differences versus large individual variation, the interplay of socialization and biology, and the h

  2026-06-27 · 13 min read #psychology#relationships#gender#communication#science

• What Makes a Good Partner: The Qualities That Build a Lasting Relationship

  A survey of the universal qualities that build lasting relationships. We look at honesty and respect, emotional maturity, communication, and trust as human virtues that have nothing to do with sex, along with insights fr

  2026-06-27 · 9 min read #relationships#psychology#self-growth#communication#trust

• PBR Texturing and Rendering — The Science of Light and Material

  Starting from UV unwrapping, this guide organizes the core PBR concepts of metallic, roughness, base color, and normal maps. It covers texture map types and baking, the rendering pipeline split between rasterization and

  2026-06-27 · 18 min read #pbr#texturing#rendering#uv-unwrapping#lighting

• Agile, Scrum, Kanban — A Practical Framework Comparison

  From the Agile Manifesto and its 12 principles to Scrum roles, events, and artifacts, and Kanban flow with WIP limits, this is a practical comparison. It also covers estimation, fake-agile anti-patterns, and a step-by-st

  2026-06-27 · 16 min read #agile#scrum#kanban#sprint#story-points

• The Skeleton of Kanji — Radicals and the Logic of On and Kun Readings

  Kanji feels overwhelming not because of the count but because the structure is hidden. This guide shows how characters are assembled from a meaning radical and a sound component, why on and kun readings exist and how the

  2026-06-27 · 24 min read #japanese#kanji#radicals#onyomi#kunyomi

• A Deep Dive into PostgreSQL Architecture — From MVCC to Vectors

  A deep look at the internal architecture of PostgreSQL, from process and memory structure to MVCC, indexes, query execution, replication, partitioning, and extensions like pgvector. We unpack the core mechanisms with ASC

  2026-06-27 · 11 min read #postgresql#database#mvcc#architecture#indexing

• Two Brains for a Humanoid — GR00T N1 and Helix

  A VLA for humanoid robots must combine fast reflexes with slow deliberation. Centered on NVIDIA GR00T N1 and Figure AI Helix, we organize the dual-system architecture that combines fast low-level control (System 1) with

  2026-06-27 · 15 min read #ai-papers#robotics#humanoid#groot-n1#helix

• Robots That See, Hear, and Move — A Review of VLA Models RT-2 and OpenVLA

  Vision-Language-Action (VLA) models take camera images and natural-language instructions and output robot actions directly. Centered on RT-2, Open X-Embodiment, and OpenVLA, this post organizes the VLA paradigm: its idea

  2026-06-27 · 15 min read #ai-papers#robotics#vla#rt-2#openvla

• Diffusion Policy and π0 — The Secret Behind Smooth Robot Behavior

  Going beyond the limits of discrete action tokens, we examine two streams that generate actions as continuous values. Diffusion Policy generates actions via denoising, and π0 produces high-frequency continuous actions wi

  2026-06-27 · 14 min read #ai-papers#robotics#diffusion-policy#pi0#flow-matching

• Communication for Engineers: How to Get Technical Decisions Made

  Good ideas usually fail not because they are bad but because they are delivered badly. This post covers writing design docs and RFCs, leading with the problem instead of the solution, tailoring the message to executives

  2026-06-27 · 13 min read #career#communication#engineering

• Building CLIs in Rust: clap and the Secrets of ripgrep, fd, bat

  Over the past few years the command line has been quietly rewritten in Rust: ripgrep, fd, bat, eza, zoxide, bacon. This post explains why that renaissance happened and what you actually need to build a CLI yourself. We d

  2026-06-27 · 9 min read #rust#cli#tools

• The Birthday Paradox and Hash Collisions — Why 23 People Means a 50% Match

  Just 23 people in a room, and the odds that two share a birthday cross 50%. At the root of this counterintuitive result is a √N approximation, and the same math runs through UUID and hash collision probability, why 128 b

  2026-06-27 · 11 min read #math#hashing#fundamentals

• Training Vision LLMs — How to Teach Input and Output

  A vision-language model is trained in stages, from alignment pretraining to instruction fine-tuning. We organize what gets taught and how, from the angle of the training pipeline: vision encoder freezing strategy, data c

  2026-06-26 · 17 min read #mlops#vision-language-model#multimodal#training#instruction-tuning

• LLM Inference Serving 2026 — Comparing vLLM, SGLang, and TensorRT-LLM

  A clear overview of LLM inference serving in 2026. From core principles such as the difference in nature between prefill and decode, continuous batching, and paged KV cache, to a strengths-and-weaknesses comparison of vL

  2026-06-26 · 15 min read #llm-serving#vllm#sglang#tensorrt-llm#inference

• Making Inference Fast — Speculative Decoding and Throughput Optimization

  From the fundamental reason LLM decode is slow, to how speculative decoding boosts speed, variants such as Medusa and EAGLE, chunked prefill and prefill/decode disaggregation, the latency versus throughput trade-off, and

  2026-06-26 · 13 min read #speculative-decoding#throughput#inference#mlops#latency

• Serving Multimodal LLMs — The New Challenges Image Input Creates

  From how multimodal LLM serving differs from text-only serving, to the added vision-encoder stage, variable visual token counts, prefill cost spikes, the difficulty of multimodal KV cache and batching, latency decomposit

  2026-06-26 · 14 min read #mlops#multimodal#llm-serving#vllm#kv-cache

• Understanding Positional Encoding — From Sine Waves to RoPE

  Starting from why Transformers need positional information, this article explains sinusoidal, learned, and relative positional encodings step by step, then RoPE and ALiBi. It connects length extrapolation and context ext

  2026-06-26 · 17 min read #llm#positional-encoding#rope#alibi#long-context

• Dissecting the Transformer — From Attention to KV Cache

  A from-scratch breakdown of the Transformer: self-attention, multi-head, positional encoding, the FFN, and residual connections with normalization. It connects tensor shapes and parameter counts, causal masking, encoder/

  2026-06-26 · 16 min read #llm#transformer#attention#positional-encoding#kv-cache

• The Evolution of Attention — MQA, GQA, FlashAttention, and Long Context

  We analyze the memory and compute cost of standard attention, then explain how MQA and GQA shrink the KV cache and how FlashAttention optimizes IO. We compare sliding-window and long-context techniques and trace how all

  2026-06-26 · 18 min read #llm#attention#flashattention#gqa#mqa

• Vision LLM Architecture — How an Image Becomes Language

  A vision-language model processes an image with a vision encoder, then passes it through a projector to produce tokens an LLM can read. From patch embedding to arbitrary-resolution handling, we trace the full path by whi

  2026-06-26 · 20 min read #llm#vision-language-model#multimodal#vit#qwen2-vl